This paper presents an EEG study for coherence and phase synchrony in mild cognitive impairment (MCI) subjects. MCI is characterized by cognitive decline, which is an early stage of Alzheimer’s disease (AD). AD is a neurodegenerative disorder with symptoms such as memory loss and cognitive impairment. EEG coherence is a statistical measure of correlation between signals from electrodes spatially separated on the scalp. The magnitude of phase synchrony is expressed in the phase locking value (PLV), a statistical measure of neuronal connectivity in the human brain. Brain signals were recorded using an Emotiv Epoc 14-channel wireless EEG at a sampling frequency of 128 Hz. In this study, we used 22 elderly subjects consisted of 10 MCI subjects and 12 healthy subjects as control group. The coherence between each electrode pair was measured for all frequency bands (delta, theta, alpha and beta). In the MCI subjects, the value of coherence and phase synchrony was generally lower than in the healthy subjects especially in the beta frequency. A decline of intrahemisphere coherence in the MCI subjects occurred in the left temporo-parietal-occipital region. The pattern of decline in MCI coherence is associated with decreased cholinergic connectivity along the path that connects the temporal, occipital, and parietal areas of the brain to the frontal area of the brain. EEG coherence and phase synchrony are able to distinguish persons who suffer AD in the early stages from healthy elderly subjects.

No abstract

Brainwave is widely used as an indicator of brain activity and can be detected by electroencephalography (EEG). The development of EEG device has become more advanced along with the invention of low-cost tiny electronic modules and wireless technology. This research aimed to develop a low-cost wireless modular device for brainwave acquisition based on Arduino microcontroller. The system was designed into sensor block for brainwave receiver and conditioning, and mainboard block for data processing. Dry-active electrode was developed as the sensor, followed by preamplifier module which was also installed at the sensor block. Active filter and DRL circuits were developed on the mainboard part. Arduino UNO was used as the main processor of the device. The developed modules were then evaluated using signal generator to examine the module characteristics and consistency. As the result, the preamplifier module was detected to reach 40.34 dB on gain ability. The cutoff frequency on the active filter module was calculated on 31 Hz. Furthermore, Arduino UNO was identified to have a consistency on input and output voltage.

The usage of wireless system and dry electrode on electroencephalography (EEG) device becomes widely demanding, particularly in commercial purposes. While the wireless system is needed for lesser cable interference and practical function for mobility, the dry electrode is very important for signal consistency in longer period of brainwave acquisition. Previously, a wireless EEG device was developed in our laboratory; however, the evaluation of the acquired brainwave is needed for further usage and development. This research aimed to compare the signal acquired by the developed EEG device using Emotiv Insight device as a benchmark, which is already an established wireless and dry electrode-based EEG on the market. The brainwave acquisitions were conducted on the subject while resting with eyes closed. AF3 and AF4 of frontal lobe channels were used as the electrode placements. The results were then characterized using frequency band analysis, SNR comparison, and general signal inspection. The result showed that the signal patterns on both devices were visually similar. A minor difference on the amplitude scale can be adjusted by normalization method. The result of alpha band calculation, which is normally detected in resting activity, found similar on both devices. Furthermore, the SNR result from developed device was considered fairly close to the benchmarking device. This study showed that developed EEG device was considered comparable to Emotiv Insight in detecting alpha band extracted from resting frontal lobe, as well as in the brainwave filtering process and accuracy.

Music has an important role in our life nowadays. Music can affect emotions and brain activity that can be measured through brain waves as electrical signals produced by neurons to carry sensory and cognitive information. In this study, brain waves for 10-12 normal male-non musician undergraduate students under three kinds of treatment are read using wireless electroencephalography (EEG) with 14 channels. For the first treatment, EEGs data are recorded when the subjects are in relax condition, i.e. rest and listening music. For the second treatment, subjects were stimulated with music in two loudness levels and for the last treatment subjects were stimulated with two different tempos of a song. From all subjects of this work, it was obtained that the right brain hemisphere is more active when listening music (significance level of 0.02). The average power spectra slightly increase with increasing music loudness (significance level of 0.35-0.45). Changes in musical tempo cause a decrease of the power spectra of alpha and beta bands (significance level of 0.25-0.30).

No abstract

In the beginning of this recent years, the number of children with Autism Spectrum Disorders (ASD) in Indonesia has increased. Previous studies showed that brain activity of children with ASD was found to be prominently different in frontal and temporal lobes. One of the known methods to measure brain activity in both normal and disorder brain condition is the measurement of brainwaves using electroencephalography (EEG). Therefore, this study aimed to analyze the brainwaves of frontal and temporal lobes of children with ASD, using a calculation of spectral entropy. The subjects group on this research were the children with ASD and the normal children as a control group. EEG recordings were conducted while the children from both of the group was resting and eyes closed. As a preprocessing method, data filtering and algorithm normalization were done to get EEG data. Periodogram Welch method, based on Fast Fourier Transform (FFT), was then used to calculate the power spectral entropy (PSE) of alpha and beta frequency bands. The results were show that at 14 second of children with ASD had the PSE peak. The peak that was observed in the The frontal lobe (F8) located in the right hemisphere was higher than the peak shown in the temporal lobe (T8). However, the peak of left hemisphere T7 was higher than F7.On the control object, T8 had a peak with values that was greater than the peak of F8 located in the right hemisphere. Meanwhile, for the left hemisphere, the peak of F7 was greater than T7. Based on these results, it could be concluded that the ASD object and the control object had different frontal and temporal lobe PSE conditions.

No abstract